Service-Induced Congestion in Memory-Constrained LLM Serving
In large language model (LLM) serving, each request accumulates persistent graphics processing unit (GPU) memory during service as its key-value cache grows with every generated token. Under high concurrency, aggregate memory usage therefore increases endogenously over time: the service process itself creates future capacity pressure. When memory capacity is exceeded, systems evict active requests, discarding cached state and restarting them later, which wastes computation and reduces throughput. We develop a discrete-time dynamical model of memory-constrained LLM inference that captures admission, memory growth, and eviction under continuous batching. In the saturated-input regime, the s...
| Authors | Ruicheng Ao, Jing Dong, Gan Luo et al. |
|---|---|
| Primary category | math.OC |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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